This tutorial will show a bag of basic recipes in order to efficiently manipulate and process images in the form of NumPy arrays.
|Target audience||Scientists and engineers working with images|
|Prerequisites||Being able to code Python scripts and use an interactive Python shell + some knowledge of NumPy|
|Software requirements||IPython, NumPy, SciPy, Matplolib, and optionally scikits.learn and scikits.image|
- I/O: how to open different image formats, how to open raw images, how to deal with very large raw files.
- Basic visualization of images, and interaction with image data
- Transforming images: changing the size, resolution, orientation of an image; image filtering; image segmentation.
- Extracting information from images: measuring properties of segmented objects; image classification
- (Very brief) discussion on some advanced image processing Python modules (scikits.image, CellProfiler, ITK, ...)
This tutorial will by no means be a course on digital image processing. It is rather a bag of tricks on how to tinker with images, and how to use the goodies of Python/NumPy/SciPy to make this task easier. A large part of the talk will be devoted to hands-on exercises using the NumPy, SciPy and Matplotlib modules. Some other modules will be mentioned during the tutorial for more advanced uses.